class_docstr: Represents an autoregressive integrated moving-average (ARIMA) model.
  
  An [autoregressive moving-average (ARMA)](
  https://en.wikipedia.org/wiki/Autoregressive_moving_average_model) process is
  defined by the recursion
  
  ```
  level[t + 1] = (
      level_drift
      + noise[t + 1]
      + sum(ar_coefficients * levels[t : t - order : -1])
      + sum(ma_coefficients * noise[t : t - order : -1]))
    noise[t + 1] ~ Normal(0., scale=level_scale)
    ```
  
  where `noise` is an iid noise process. An integrated ([ARIMA](
  https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average))
  process corresponds to an ARMA model of the
  `integration_degree`th-order differences of a sequence, or equivalently,
  taking `integration_degree` cumulative sums of an underlying ARMA process.
code: def _make_state_space_model(
      self,
      num_timesteps,
      param_map=None,
      initial_state_prior=None,
      **linear_gaussian_ssm_kwargs,
  ):
      if initial_state_prior is None:
          initial_state_prior = self.initial_state_prior
  
      def maybe_make_dummy_prior(integration_degree):
          """"""
          integration_steps_remaining = (
              self.integration_degree - integration_degree
          )
          if integration_steps_remaining == 0:
              return initial_state_prior
          dtype = initial_state_prior.dtype
          return mvn_diag.MultivariateNormalDiag(
              loc=tf.zeros(
                  [self.latent_size - integration_steps_remaining],
                  dtype=dtype,
              ),
              scale_diag=tf.ones(
                  [self.latent_size - integration_steps_remaining],
                  dtype=dtype,
              ),
          )
  
      arma_kwargs = dict(
          linear_gaussian_ssm_kwargs, **(param_map if param_map else {})
      )
      arma_kwargs["ar_coefficients"] = arma_kwargs.get(
          "ar_coefficients", []
      )
      arma_kwargs["ma_coefficients"] = arma_kwargs.get(
          "ma_coefficients", []
      )
  
      current_integration_degree = 0
      ssm = AutoregressiveMovingAverageStateSpaceModel(
          num_timesteps=num_timesteps,
          initial_state_prior=maybe_make_dummy_prior(
              current_integration_degree
          ),
          name=self.name,
          **arma_kwargs,
      )
      for _ in range(self.integration_degree):
          current_integration_degree += 1
          ssm = IntegratedStateSpaceModel(
              ssm,
              name=self.name,
              initial_state_prior=maybe_make_dummy_prior(
                  current_integration_degree
              ),
              **linear_gaussian_ssm_kwargs,
          )
      return ssm
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.bijectors import tanh
  from tensorflow_probability.python.distributions import linear_gaussian_ssm
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.sts.components.autoregressive_moving_average import AutoregressiveMovingAverageStateSpaceModel
  from tensorflow_probability.python.sts.internal import missing_values_util
  from tensorflow_probability.python.sts.internal import util
  from tensorflow_probability.python.sts.structural_time_series import Parameter
  from tensorflow_probability.python.sts.structural_time_series import StructuralTimeSeries
label_desc: Use image node_id 4 for calling the AutoregressiveIntegratedMovingAverage obj's underlying member method code with example usage: obj._make_state_space_model(num_timesteps, param_map, initial_state_prior) and returns: ssm, mvn_diag, initial_state_prior